1. Field of the Invention
The present invention relates to the recognition of fingerprints, which is a well tried and tested biometric technique for the identification of individuals. The present invention more specifically relates to the processing of a digital image representing a fingerprint to enable subsequent comparison thereof.
2. Discussion of the Related Art
The fingerprint recognition is performed by examining the papillary line arrangement which forms characteristic points called minutiae. The present invention relates to the processing of a fingerprint image to extract its minutiae therefrom.
As known, fingerprint analysis concentrates on two types of minutiae, especially minutiae of branch type and of end type which, from a statistical point of view, are the most frequent.
FIG. 1 very schematically shows an automatic fingerprint recognition system of the type to which the present invention applies. Such a system is essentially formed of a sensor 1 (formed, for example, of an optical device of digitizer type) connected to a processing unit 2 (PU) in charge of interpreting the measurement results. Processing unit 2 provides, over a connection 3, a signal of authentication or non-authentication of a finger D laid on sensor 1. Processing unit 2 generally has the function of shaping the digital image generated by sensor 1 and of analyzing this image to compare it with one or several images contained in a reference database.
FIG. 2 illustrates, in the form of blocks, a conventional example of a method for extracting minutiae from a digital fingerprint image. Sensor 1 provides a digital image 10 in which grooves (peaks and valleys) of the papillary arrangement are shown in shades of grey. The processing unit then performs (block 11, SB-DIV) a division of the image into image regions or blocks. The block orientation is then determined (block 12, SB-ORIENT) and a filtering is applied thereto to smooth the image (block 13, 2D-FILTER). The use of an oriented filtering enables reducing the calculation time with respect to a global filtering. After the filtering, the image (in fact, the different blocks) is binarized (block 14, BINAR), that is, converted from grey levels to black and white levels, for example, by thresholding, by variance or mean value calculation. The binarization aims at avoiding possible discontinuities of the grooves which would be linked to the image shooting (contrast, brightness differences, etc.). Finally, the image is skeletized (block 15, SQUEL), that is, the lines representing the grooves are thinned down to suppress possible artifacts.
The final image obtained (entirely restored) is used to for the actual minutia search. This search (block 16, RECH) is performed by scanning the entire obtained image skeleton and by detecting the presence of branches or ends. Generally, for each pixel of the skeletized image, the level (black or white) of the eight pixels surrounding it is examined with respect to its own level. In fact, according to whether the skeletization provides a positive or negative image, it is decided to examine the black pixels or the white pixels. If a single one of the eight pixels surrounding it is of the same level as the current (central) pixel, an end has been found, the coordinates of which represent its position in the papillary arrangement. If exactly two of the eight pixels are of the same level as the current pixel, the three pixels belong to a same groove. If exactly three pixels are of the same level as the current pixel, the current pixel most likely represents the coordinates of a minutia of branch type.
Since a relatively reduced number of identical minutiae (from 8 to 17) between two prints is enough to accept the probability of an identity between the two prints, account is generally not taken of minutiae which are too close to one another and which risk resulting from imperfections in the image shooting or in the digitizing techniques. In practice, the interval between two minutiae is determined along the same groove and no account is taken of the minutiae which are not distant by at least a predetermined number of pixels.
The above-described techniques are well known and currently used in image processing. For example, reference may be made to article “Fingerprint Image Enhancement: Algorithm and Performance Evaluation” by Lin Hong, Yifei Wan and Anil Jain, published in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, N°8, August 1998, which is incorporated herein by reference.
A disadvantage of current techniques is that they require storage of the entire image at each step of the image processing. In particular, the filtering, the binarization, and the skeletization are performed one after the others for the entire image. Even if these processings are carried out by regions, the entire image is restored before searching the minutiae.